Econ 123

Lecture 2: Motivating Example

Edward Vytlacil

Yale University

Today: LFP and Wages for Women

  • Consider historical trends in labor force participation (LFP) and earnings of women over 20th century.

  • Why did these trends occur?

    • possible explanations push economists to study fertility, marriage, within-household bargaining, education, discrimination. . .
    • What are the relevant econometric techniques to evaluate proposed explanations?

Today: LFP and Wages for Women

  • Consider historical trends in labor force participation (LFP) and earnings of women over 20th century.

  • Why did these trends occur?

    • evaluating potential explanations involves answering “what-ifs”, e.g.,
      • what if women had not gained increased access to birth control/abortion?
      • what if women now loose access to birth control/abortion?

Goals of today’s lecture:

  • illustrate interplay of economics, econometrics, data;
  • illustrate scope of economics.

We will return to consider relevant econometric methods throughout the course.

Drawing upon work by Goldin

Lecture draws heavily from work by Claudia Goldin, especially:

Claudia Goldin

Also motivated by Economists Amicus Brief in Dobbs v. Jackson Women’s Health

See Blau and Kahn (2017) for recent literature review.

Descriptive Statistics

  • Descriptive Statistics

  • Economic Models

  • Interpreting Changes in Measured Wage Gap

  • Determining Causality

Goldin (2006a): Quiet Revolution

  • Goldin (2006a) presents description statistics on women’s labor market outcomes.

  • Labor force participation of women steadily increased through 20th century, more rapidly 1960-1990.

Labor Force Participation. Figure from Goldin (2006b), published as Goldin (2006a).

Goldin (2006a): Quiet Revolution

  • Goldin (2006a) presents description statistics on women’s labor market outcomes.

  • The earnings gap between men and women, among those employed full-time, shrank dramatically starting around 1980.

Woman’s Earnings as a Percentage of Men’s Earnings, Among Those Employed Full-Time. Figure from Goldin (2006b), published as Goldin (2006a).

Goldin (2006a): Quiet Revolution

  • Goldin (2006a) presents description statistics on women’s labor market outcomes.

  • Occupations of young, college-educated women changed dramatically after 1970.

Occupations of college graduate women aged 30 to 34. Figure from Goldin (2006b), published as Goldin (2006a).

Goldin (2006a): Quiet Revolution

  • Goldin (2006a) presents description statistics on women’s educational outcomes.

  • Rapid increase in college attendence/graduation for women relative to men starting in 1950, have now overtaken men.

Figure from Goldin (2006b), published as Goldin (2006a).

Goldin (2006a): Quiet Revolution

  • Dramatic increase of women attending professional schools starting around 1970.

  • Goldin (2005) documents dramatic change in college majors for women starting around 1970, switching towards business and other traditionally male-intensive majors.

Figure from Goldin (2006b), published as Goldin (2006a).

Economic Models

  • Descriptive Statistics

  • Economic Models

  • Interpreting Changes in Measured Wage Gap

  • Determining Causality

Goldin (2006a): Quiet Revolution

  • Why did these dramatic changes happen?

  • Starting point: Economic models of labor supply, wages.

    • Labor supply models:

      • substitution and income effects for labor supply,

      • role of fertility? marriage?

      • decision making within the household?

Goldin (2006a): Quiet Revolution

  • Why did these dramatic changes happen?

  • Starting point: Economic models of labor supply, wages.

    • Labor supply models:

      • substitution and income effects for labor supply,

      • role of fertility? marriage?

        • role of access to birth control, abortion?

      • decision making within the household?

        • role of changes in divorce law?

Goldin (2006a): Quiet Revolution

  • Why did these dramatic changes happen?

  • Starting point: Economic models of labor supply, wages.

    • Wages:

      • human capital model of wages
        (education, work-experience),

      • discrimination, either “taste-based” or “statistical”

Goldin (2006a): Quiet Revolution

  • Why did these dramatic changes happen?

  • Starting point: Economic models of labor supply, wages.

    • Wages:

      • human capital model of wages
        (education, work-experience),

        • role of anti-discrimination legislation?
        • role of access to birth control, abortion?

      • discrimination, either “taste-based” or “statistical”

        • role of anti-discrimination legislation?

Goldin (2006a): Quiet Revolution

  • Dynamic models

    • Current wage and LFP depends on past choices
      (past work experience, education, fertility…),

    • those past choices depend on expectations, e.g. education choices depend on expectations of fertility, LFP, etc.

What do economists study and why?

  • LFP and earnings of women is a topic for economics.

  • Models of LFP and earnings naturally leads to studying:

    • fertility,

    • marriage,

    • divorce,

    • within household decision making/bargaining,

    • education decisions,

    • discrimination,

    • political economy. . .

  • Economists use economic models and econometrics to study all of these topics.

Interpreting Changes in Measured Wage Gap

  • Descriptive Statistics

  • Economic Models

  • Interpreting Changes in Measured Wage Gap

  • Determining Causality

Wage-gap decline:
Regression Adjustment

  • Could trends in wages among working women be due in part to changes in their observable characteristics, e.g., more women going to college? more work experience?

    • Use regression adjustment to study,

      • We will cover regression analysis extensively in this course.

Wage-gap decline: Selection

  • Selection into labor force:
    we only observe wages of women who work.

    • Women who work might differ in unobserved ways from women who don’t work.

    • could trends in wages among working women be driven in part by which women work?

    • we will return to talk about selection later in this course.

Determining Causality

  • Descriptive Statistics

  • Economic Models

  • Interpreting Changes in Measured Wage Gap

  • Determining Causality

Econometric Issues: Endogeneity

  • Endogeneity

    • Fertility, education, labor force participation, etc,
      jointly determined.

    • Complicates separating causation from correlation.

      • e.g., negative correlation between number of child and mother’s labor supply, which way does the causation run?

Econometric Issues: Endogeneity

  • Endogeneity

    • Fertility, education, labor force participation, etc,
      jointly determined.

    • Complicates separating causation from correlation.

    • relevant methodologies include natural-experiment approaches such as instrumental variables, we will study later in this course.

Econometric Issues: Timing

Econometric Issues: Timing

  • Econometrics, natural-experiment approach: event study

    • combine across-time and across-group variation,

    • e.g., diff-in-diff and triple-diff.

  • Works well for some events, not for others:

    • works well for birth control pills, abortion by exploiting cross-state variation in timing of access.
    • not applicable for analyzing effect of changes in social norms.

Econometric Issues: discrimination

  • Economics definition of discrimination:

    • Employers basing wages (or hiring decisions) in part on an observed characteristic that is not relevant for productivity, conditional on observed characteristics that are relevant for productivity.

  • Complicating issue: Relevant worker characteristics today can depend on past discrimination against worker or on worker’s expectations of future discrimination.

Econometric Issues: discrimination

  • Economics definition of discrimination:

    • Employers basing wages (or hiring decisions) in part on an observed characteristic that is not relevant for productivity, conditional on observed characteristics that are relevant for productivity.

  • Same concept used for discrimination in loans, use of police force, judicial decisions (e.g., bail, sentencing, parole), etc., with same complicating issue.

Econometric Issues: discrimination

How to measure/detect discrimination?

  • Can use regression adjustment for characteristics the researcher observes in the data.

  • Limitation:

    • discrimination is based on what employer observes, not on what we observe in the data.

    • problem of omitted variable bias, we will discuss extensively in this class.

Econometric Issues: discrimination

How to measure/detect discrimination?

  • Natural experiment?

  • Goldin and Rouse (2000): Blind auditions

    • Most major orchestras switched to blind auditions in 1970s, 1980s

    • blind audition has candidates audition behind a screen, so that sex of candidate unknown to jury.

Econometric Issues: discrimination

How to measure/detect discrimination?

  • Natural experiment?

  • Goldin and Rouse (2000): Blind auditions

    • Orchestras made switch at different times.

    • Goldin and Rouse (2000) exploit variation in timing of switch to estimate effect of auditions being blind.

    • find blind auditions increase probability of woman musician being hired by 30%.

Econometric Issues: discrimination

How to measure/detect discrimination?

  • Actual experiment?

  • Traditional audit studies

    • Use pairs of actors, one minority and one non-minority, with similar relevant attributes.

    • Send a pair of actors to apply for each job (or loan, etc)

    • See if differences in outcomes for minority vs non-minority actors.

Econometric Issues: discrimination

How to measure/detect discrimination?

  • Actual experiment?
  • Traditional audit studies

    • Example: Neumark, Bank, and Van Nort (1996) discrimination in restaurant hiring.

    • Advantages of audit studies? limitations?

Econometric Issues: discrimination

How to measure/detect discrimination?

  • Actual experiment?

  • Correspondence studies:

    • Create fictitious resumes, and randomly assign gender/minority status as signaled by name of applicant.

    • Send pairs of fictitious resumes to job or rental advertisements.

    • Measure difference in outcomes (typically call-backs) for applications with minority vs. non-minority names.

Econometric Issues: discrimination

How to measure/detect discrimination?

  • Actual experiment?

  • Correspondence studies:

    • Famous example: Bertrand and Mullainathan (2004)

    • Advantages of correspondence studies? limitations?

This course, we will

  • cover regression analysis, instrumental variables, event studies, etc., in depth,

  • cover various applications, including related to LFP/fertility and to discrimination.

What’s next

What’s next

  • First quiz: Friday January 27
    • covering material from Handouts 1 and 2, and material on inference.
    • First problem set assigned Thursday January 26.

Econ 123: Lecture 2

Bertrand, Marianne, and Sendhil Mullainathan. 2004. “Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination.” American Economic Review 94 (4): 991–1013.
Blau, Francine D, and Lawrence M Kahn. 2017. “The Gender Wage Gap: Extent, Trends, and Explanations.” Journal of Economic Literature 55 (3): 789–865.
Goldin, Claudia. 2005. “From the Valley to the Summit: A Brief History of the Quiet Revolution That Transformed Women’s Work.” Regional Review 14 (3): 5–12.
———. 2006b. “The Quiet Revolution That Transformed Women’s Employment, Education, and Family.” National Bureau of Economic Research Cambridge, Mass., USA.
———. 2006a. “The Quiet Revolution That Transformed Women’s Employment, Education, and Family.” American Economic Review 96 (2): 1–21.
Goldin, Claudia, and Lawrence F Katz. 2002. “The Power of the Pill: Oral Contraceptives and Women’s Career and Marriage Decisions.” Journal of Political Economy 110 (4): 730–70.
Goldin, Claudia, and Cecilia Rouse. 2000. “Orchestrating Impartiality: The Impact of" Blind" Auditions on Female Musicians.” American Economic Review 90 (4): 715–41.
Hogg, Robert V, Elliot A Tanis, and Dale L Zimmerman. 2020. Probability and Statistical Inference. 10th ed. Pearson.
Neumark, David, Roy J Bank, and Kyle D Van Nort. 1996. “Sex Discrimination in Restaurant Hiring: An Audit Study.” The Quarterly Journal of Economics 111 (3): 915–41. https://doi.org/10.2307/2946676.
Wooldridge, Jeffrey M. 2020. Introductory Econometrics: A Modern Approach. 7th ed. Cengage Learning.